Bibliographic record
Abstract
We make the rst use of MARF of fast signal-processing and related techniques for LifeCLEF 2015 identication tasks. We build an application based on a pattern recognition pipeline implemented in an open-source Modular A* Recognition Framework (MARF). MARF is also the name of the team in this submission. For that purpose to test and select among available algorithm a set of suitable algorithms. This is the rst implementation of the application we call MARFCLEFApp tested on a very small subset of algorithms available. The approach covers Bird-, Plant-, and FishCLEF tasks. It was expected the bird task would be the best for the presented approach given MARF's original intent for audio recognition. However, lack of enough run-time it turned out to be the worst one and is under the investigation. Processing FishCLEF however yield the best of the three tasks, which was expected to be the worst. Team MARF's results for FishCLEF were the 2nd team after with the Run 1 being the best of the three.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".